Person Verification and Gender Detection Based On Single-hand Image Using Feature Extraction

Biometric authentication has become increasingly important in security and identity management applications. This study proposes a multi-stage biometric system capable of performing both person verification and gender detection using single-hand images. The proposed framework integrates multiple feature extraction and classification strategies within a unified experimental setup. Three feature extraction methods (Zernike moments (ZM), wavelet transform (WT), and Hough transform (HT)) are employed in single and hybrid combinations, yielding five distinct feature extraction configurations. The classification stage includes five classical machine learning models, a triple hybrid ensemble model, and one deep learning model, including Decision Trees (DT), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), Support Vector Classifier (SVC), a triple hybrid model (DT+GB+GNB), and a Deep Neural Network (DNN), enabling a comprehensive comparative analysis. Experimental results show that the SVC and DNN models achieved the highest performance, yielding person verification accuracy of 97.86% and gender detection accuracy of 98.00%, respectively. The findings of this study demonstrate the feasibility of a unified hand-based biometric framework and provide valuable insights for the design of robust and scalable biometric authentication systems.